Bayesian calibration of Vehicle-Terrain Interface algorithms for wheeled vehicles on loose sands. (June 2017) - Dettwiller, Ian; Rais-Rohani, Masoud; Vahedifard, Farshid; Mason, George L.; Priddy, Jody D.
Highlights: Vehicle-Terrain Interface algorithms for wheeled vehicles on sand were calibrated. Bayesian calibration was used to calibrate drawbar pull, traction, and motion resistance equations. Calibration resulted in validated improvements for all three equations. Improvements were verified through performance on a test dataset. Abstract: The Vehicle-Terrain Interface (VTI) model is commonly used to predict off-road mobility to support virtual prototyping. The Database Records for Off-road Vehicle Environments (DROVE), a recently developed database of tests conducted with wheeled vehicles operating on loose, dry sand, is used to calibrate three equations used within the VTI model: drawbar pull, traction, and motion resistance. A two-stage Bayesian calibration process using the Metropolis algorithm is implemented to improve the performance of the three equations through updating of their coefficients. Convergence of the Bayesian calibration process to a calibrated model is established through evaluation of two indicators of convergence. Improvements in root-mean square error (RMSE) are shown for all three equations: 17.7% for drawbar pull, 5.5% for traction, and 23.1% for motion resistance. Improvements are also seen in the coefficient of determination ( R 2 ) performance of the equations for drawbar pull, 2.8%, and motion resistance, 2.5%. Improvements are also demonstrated in the coefficient of determination for drawbar pull, 2.8%, and motion resistance, 2.5%, equations, while the calibrated traction equation performs similar to the VTI equation. A randomly selected test dataset of about 10% of the relevant observations from DROVE is used to validate the performance of each calibrated equation.